How Should a Prompt Optimizer Spend a Tight Budget? BudgetAPO with Noise-Adaptive Evaluation
This study addresses the vulnerability of existing Automatic Prompt Optimization (APO) methods to resource exhaustion and noise interference under strict invocation budgets. We propose BudgetAPO, a single-stage optimization framework that introduces a short-probe-based noise-adaptive slicing mechanism, integrated with paired comparative statistical testing and a reflective joint rewriting strategy, to achieve efficient prompt optimization within limited budgets. Experiments demonstrate that BudgetAPO attains state-of-the-art performance across seven benchmarks and five models. Notably, under a stringent constraint of only 250 invocations, it achieves a failure rate as low as 13%, significantly outperforming baselines such as GEPA. This work provides an efficient and reliable solution for prompt optimization in budget-constrained scenarios.